Prediction of Employee Retention using Cassandra and Ensemble Learning
Shubham Karande1, Ajay Shelake2, Sivagami M3, Sharon Sophia4
1Shubham Karande, M. Tech SCSE, VIT University, Chennai (Tamil Nadu) – 600127, India.
2Ajay Shelake, M. Tech SCSE, VIT University, Chennai (Tamil Nadu) – 600127, India.
3Dr. Sivagami M, Associate Professor, SCSE, VIT University, Chennai (Tamil Nadu) – 600127, India.
4Dr. Sharon Sophia, Assistant Professor, VITBS, VIT University, Chennai (Tamil Nadu) – 600127, India.
Manuscript received on 01 April 2019 | Revised Manuscript received on 06 May 2019 | Manuscript published on 30 May 2019 | PP: 808-812 | Volume-8 Issue-1, May 2019 | Retrieval Number: F2388037619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Employee turnover is now becoming a major problem in IT organizations, telecommunications and many other industries. Why employees leave the organization? is the question raising among many HR managers. Employees are the most important assets of an organization. Hiring new will always take more efforts and cost rather than retaining the old ones. This paper focuses on finding the key features of voluntary employee turnover and how they can be overcome well before time. The problem is to classify whether an employee will leave or stay. Data is taken from Kaggle. The proposed work will uses ensemble learning to solve the problem, rather than focusing on a single classifier algorithm it will combine weak learning algorithms to get a better ensemble model. We have used Cassandra to store the data in the form of table and retrieving data to perform machine algorithm on them.
Key words: Employee Retention, Cassandra, Classification, Ensemble Learning, Machine Learning.
Scope of the Article: Classification